Experimental evaluation of soft sensors in an electrical submersible pump system: Validation in an ESP-lifted oil well pilot plant

This paper proposes the implementation of model-based soft sensors to experimentally validate process variables that are difficult-to-measure in the upstream oil industry, such as fluid viscosity, productivity index, and average flow rate in the production column. The study focuses on an oil production pilot plant operated by an electrical submersible pump (ESP), which was fully instrumented with a supervisory system that collected and stored data, enabling the validation of algorithms. We evaluate the design and use of the Extended Kalman Filter (EKF), the Unscented Kalman Filter (UKF), and the Nonlinear Moving Horizon Estimator (NMHE) to monitor and estimate the process variables and compare their performance in a real environment. The results demonstrated that all three estimators EKF, UKF, and NMHE performed satisfactorily under both transient and steady -state operating conditions. However, NMHE outperformed the others by delivering lower estimation errors and better accuracy over 40 h of continuous operation at the oil well pilot plant monitored by ESP. This superior performance is quantified using the indicator root mean squared error (RMSE). With a 10-depth window, NMHE showed greater accuracy in estimating system states, achieving a global average RMSE of 0.207, in contrast to EKF's 0.282 and UKF's 0.280. Overall, the study highlights the potential of the evaluated model-based soft sensors in the upstream petroleum industry to measure challenging variables precisely.